S. Jawahar ¹, K. Nirmala ²

Affiliations
1 Quaid-E-Milleth Government College for Women, University of Madras, Chennai - 600002, IN
2 Department of Computer Science, Quaid-E-Milleth Government College for Women, Chennai - 600002, IN

Abstract

Objectives: The objective of this research work is to analyze on learner characteristics like active, reflective, group or solo learning qualities called 'learner portfolios' that are to be analyzed by a collaborative agent in an e-learning environment. Methods: This research work proposes a methodology that can determine e-learner characteristics from respective user profiles and interact with any adaptive e-learning system in an asynchronous mode. Since most of the learner characteristics are subjective, with literature support, this research work presents social survey results. This research work also introduces a collaborative agent based model for correlating learner characteristics. Findings: Adaptive e-learning systems employ software agents in asynchronous modes for various non-dependent process activities. As software agents act independently, such integrated approaches are expected to increase the efficiency. Literatures on collaborative agents that deal with learner portfolios of subjective attributes are very rare to be seen. Application/Improvement: These portfolios, if documented as learner profiles, can help e-learning systems to understand e-learners' behavior better and respond appropriately back to them.

Keywords

Adaptive e-learning, Collaborative Agents, Learner Characteristics, Software Agents.

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K. Sankar ¹, K. Nirmala ²

Affiliations
1 Manonmaniam Sundaranar University, Tirunelveli - 627012 , Tamil Nadu, IN
2 Department of Computer Science, Quaid-e-Millet College, Chennai – 600002, Tamil Nadu, IN

Abstract

Objective: Breast calcifications can be present in mammograms which are one of the most important risk indicators of breast cancer. Digital mammography is a reliable tool to detect breast cancer at the early stage with no symptoms. The objective of this research work is to classify the microcalcification patterns into benign and malignant. Methods: In this research, a novel approach is proposed for classification of microcalcifications based on shift-invariant transform, Jacobi moments and Support Vector Machines (SVM). The Jacobi moments are used to extract orthogonal features from the location of microcalcifications based on orthogonal or weighted polynomial basis which uses recurrence relation to avoid the loss of precisions. The Jacobi features will be given as input to SVM classifier for classifying the mammogram images into normal and abnormal. The abnormality will be further classified into benign or malignant. Findings: The validity of the proposed approach is evaluated using MIAS database. In the process of mammogram enhancement, the experimental results shows that shift-invariant transform achieves better results than contourlet transform. The classification rate of proposed system is 98.65% for normal, 95.80% for benign and 93.35% for malignant accuracy. The performance of the proposed approaches measured by sensitivity, specificity and confusion matrix. The measurement of performance is 0.84 sensitivity and 0.95 specificity for stage1 and 0.85 sensitivity and 0.83 specificity for stage 2. Application/Improvement: The results show that our proposed approach gives high level of accuracy for classification of microcalcification. This approach is very useful to avoid unnecessary biopsy.

Keywords

Breast Calcifications, Jacobi Moments, Orthogonal Features, Support Vector Machines.

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J. Adamkani ¹, K. Nirmala ²

Affiliations
1 Quaid-E-MillathGovt College for Women, Chennai - 600002, Tamil Nadu, IN
2 Department of Computer Science, Quaid-E-MillathGovt College for Women, Chennai - 600002, Tamil Nadu, IN

Abstract

Objectives: In recent scenario, online social networks such as Face book, Twitter and Google+ have become one of the fastest emerging e-services. There are several issues affected these e-services. Since it is emerging service and reliability to communicate, in social networks privacy is often a key concern by the users. Since millions of people are willing to interact with others, it is also a new attack ground for malware creators. Some users and pages spreading malicious content and sending spam messages by taking advantage on the users’ inherent trust in their relationship network. Methods:This proposed work handles the most prevalent issues and threats targeting different social networks recently. And finally finds the authentication scheme for those attacks. This proposes a detecting and blocking scheme for social sites using data mining techniques. Findings: This system helps to detect suspicious URLs for social network by considering the following parameters, i).Text and keywords appears in the URL. ii). URL descriptions iii). Detection of scam messages which is done in manual script attacks on social sites. Application/Improvement: This performs two techniques which are message filtering and MLE (Maximum Likelihood Estimation).

Keywords

Feature Selection, Machine Learning, Security, Social Network

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K. S. Sunitha ¹, K. Nirmala ¹

Affiliations
1 Department of Computer Science, Quaid-E-Millath Government College for Women(A), University of Madras, Chennai - 600002, Tamil Nadu, IN

Abstract

Objectives: Proportional increase in speed of development (known as pair speed advantage) in software product development by pair programmers when compared with single programmer has been reported in literature. There is also an indication in the literature that the software defects in relation to lines of coding (known as defect density) are reduced in the case of pair programming when compared with conventional single programming technique. Correlation studies onthe contribution of application specific expert, when paired with conventional single programmer are not to be seen much in literature. Whether the findings seen in literature would hold good for small sized software developments also? Under these background this research work aims at presenting a correlation study (presentation of results) on the defect density of five chosen small sized software developments by three different programmer strategies, namely single, pair and expert programmer pair, in correlation with pair speed advantage. Methods: The research work however doesn’t present correlation coefficients and other related statistical results, as ‘correlation’as a term, is treated only for the study of relative performances. The novelty of the work is exhibited through the isolated study on the contribution of domain or application specific expert when acted as a pair alongside a relatively inexperienced s software programmer, while the domain expert need not know software programming as such, although he/she might still be a subject/domain specialist. Finding: The experimental works elaborated in the paper, which involved three s/w developer pairs on five small projects (file sizes varied from about 300 to 800 KB) were completely carried out by the control and direction of the researcher herself, in laboratory conditions as pointed out in the paper, and no external agencies were involved as the coding on the chosen applications is small when compared with huge LOC of s/w industries. These results were not sent to any other publisher for publication. Since the objective of the research work is to do a correlative study between the efforts of programmers and domain experts, MCA students and known domain experts (namely banking staff) were deployed by the researcher. The demography presented in the paper vouches the same. Application/Improvement: The paper has clearly demonstrated the performance improvement by the expert pair combination in the reduction of application specific defects (expressed in terms of defect density similar to the term pointed out by Frank Padberg et. al. 2003), when correlated with other two pairs. The paper also has shown that there is also a marginal increase in the pair speed advantage in the case of expert pair with compared with conventional programmer pair.

Keywords

Application Specific Experts, Pair Programming, Pair Speed Advantage, Software Defect Density

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R. Arunadevi ¹, K. Nirmala ²

Affiliations
1 Manonmaniam Sundaranar University, Tirunelveli - 627012, Tamil Nadu, IN
2 Department of Computer Science, Quaid-e-Millath Government College for Women, Chennai – 600002, Tamil Nadu, IN

Abstract

Objectives: Proxy Key Provisioning Tool (PKPT) is generated for dynamic key generation for dispatching secured key for protecting them from the attacks when third party assessor assessing data. Analysis: In the cloud the privately stored encrypted data retrieved by decoding it using provided key by authenticate users. While the data is stored in various cloud storage by splitting it up into different data chunks then they have to be again integrated using the decoding key that is lively generated by a cryptography algorithm. When the prevailing techniques for regenerating codes that helps in remote private assessing methodologies for ensuring its data integrity that require database holders to be online or appoint any proxy for generating privileged keys for assessors. Some type of privileges like regeneration of authentication in public assessing model only with the help of proxy which can only be semi trusted will help relieve data holder from preserving their data without being online. Findings: In our proposed method we deploy an automatic tool termed as Proxy Key Provisioning Tool (PKPT) instead of appointing a proxy server. The key will be generated dynamically and sent to the assessor whenever prompted. This key will have in built timer which will start once the key is dispatched and it have self destruction program once when the set time is over. Thus the key will be protected with strong security so the semi trusting of assessor problem will also be over. Then with the help of this generated key the third party assessor will check for data integrity over the private data from cloud.

Keywords

Cryptography Algorithm, Private Assessing, Proxy Servers, Proxy Key Provisioning Tool (PKPT), Public Assessing, Third Party Assessor

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K. Rajalakshmi ¹, K. Nirmala ²

Affiliations
1 Bharathiyar University, Coimbatore, Tamil Nadu, IN
2 Quaid-E-Millath Government College for Women, IN

Abstract

Objective: The industry of healthcare contains large information, which is difficult to process by manual methods. The large data are too valuable for extracting information and forming relationship from data mining area. Analysis: The experts and experienced doctors are not also available against the large population; sometimes symptoms are also being neglected. The existing system in the medical field is not able to extract all the information and knowledge from the heart disease database. Complex query for the healthcare practitioner to analyze the heart disease is still a challenging task. Finding: This paper is presenting a novel technique like K-Means, Weighted Associative Classifier (WAC) and Prediction Tree C5.0 for analyzing the heart disease and to sort out the existing issues. The K-Means is being used for unsupervised learning cluster within WAC. Initial centroid is being selected by the K-Means, which allow the classifier to extract the record and make a prediction for analyzing the disease with C5.0 prediction tree. The combined technology of K-Means, WAC and Prediction Tree C5.0 will provide a better, integrated, and accurate result over the heart disease prediction. The projected tool is MapReduce with Hive Database in Hadoop open source framework. Hadoop is perfectly compatible for big data projects. Improvements: The approaches for developing an Intelligent System for Heart Disease Prediction with big data mining will be very advantageous by automation of the proposed system K-Means, Prediction Tree C5.0, Weighted Association Classifier (WAC).

Keywords

Hive Database, K-Means Clustering, MapReduce, Prediction Tree C5.0, Weighted Association Classifier, Weighted Support and Confidence.

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R. Nandakumar ¹, K. Nirmala ²

Affiliations
1 R & D Centre, Bharathiar University, Coimbatore - 641046, Tamil Nadu, IN
2 Quaid-E-Millath College (W), Chennai - 600002, Tamil Nadu, IN

Abstract

Objectives: To enhance the improved security-aware packet scheduling for improving the packet level security in Mobile Ad-hoc Networks (MANET). The dependency and anonymity issues in security-aware packet scheduling are considered and removed by the proposed approach. Methods: The major challenge in MANET was the packet level security in multiple levels. The network performance was degraded due to contravene in packet level security which causes high packet loss. To overcome this challenge, an Improved Security-Aware Packet Scheduling (ISAPS) was proposed for achieving high level security and effective packet scheduling. Findings: In ISAPS, the packets from the multiple nodes were considered as independent to each other. In addition, the computational complexity and energy consumption were high. The correlation of the multiple dependency packets were degraded the network performance due to representing the dependencies and scalability. The efficiency of security was less since the source node and their packets were easily identified by the attacker. Improvements: These issues are avoided by proposed inter and intra service dependencies based ISAPS algorithm. Furthermore, the anonymity concept is included with the security-aware packet scheduling algorithm for improving the efficiency of security by secure routing protocol.

Keywords

Anonymity, Attacker, Dependency, Packet Level Security, Packet Scheduling

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